nonoJDWAOIDAWKDA/Amelia_reviewed2_ft_StyleTTS2

StyleTTS2 Fine-tuned Model

This model is a fine-tuned version of StyleTTS2, containing all necessary components for inference.

Model Details

• —Base Model: StyleTTS2-LibriTTS
• —Architecture: StyleTTS2
• —Task: Text-to-Speech
• —Last Checkpoint: epoch2nd00011.pth

Training Details

• —Total Epochs: 12
• —Completed Epochs: 11
• —Total Iterations: 2233
• —Batch Size: 2
• —Max Length: 620
• —Learning Rate: 0.0001
• —Final Validation Loss: 0.425652

Model Components

The repository includes all necessary components for inference:

Main Model Components:

• —bert.pth
• —bert_encoder.pth
• —predictor.pth
• —decoder.pth
• —text_encoder.pth
• —predictor_encoder.pth
• —style_encoder.pth
• —diffusion.pth
• —text_aligner.pth
• —pitch_extractor.pth
• —mpd.pth
• —msd.pth
• —wd.pth

Utility Components:

• —ASR (Automatic Speech Recognition)
• —epoch_00080.pth
• —config.yml
• —models.py
• —layers.py
• —JDC (F0 Prediction)
• —bst.t7
• —model.py
• —PLBERT
• —step_1000000.t7
• —config.yml
• —util.py

Additional Files:

• —text_utils.py: Text preprocessing utilities
• —models.py: Model architecture definitions
• —utils.py: Utility functions
• —config.yml: Model configuration
• —config.json: Detailed configuration and training metrics

Training Metrics

Training metrics visualization is available in training_metrics.png

Directory Structure

├── Utils/ │ ├── ASR/ │ ├── JDC/ │ └── PLBERT/ ├── model_components/ └── configs/

Usage Instructions

1. 1.Load the model using the provided config.yml
2. 2.Ensure all utility components (ASR, JDC, PLBERT) are in their respective directories
3. 3.Use text_utils.py for text preprocessing
4. 4.Follow the inference example in the StyleTTS2 documentation